Congruent validity and reliability of two metabolic systems to measure resting metabolic rate

Determine the congruent validity and intra- and inter-day reliability of RMR measures assessed by the ParvoMedics Trueone 2 400 hood dilution method (Parvo) and Cosmed K4b(2) (Cosmed) breath-by-breath metabolic systems. Participants underwent 6 RMR assessments over 2 consecutive mornings, 3 with the Parvo (Day 1: Parvo 1; Day 2: Parvo 2, 3), 3 with the Cosmed (Day 1: Cosmed 1; Day 2: Cosmed 2, 3). Measured VE, FEO(2), FECO(2), VO(2), VCO(2), kcal/day, and HR values were averaged over a minimum of 10 min. Intra- and inter-day reliability within each system was determined with RMANOVA, and congruent validity was assessed via paired sample t-tests.31 participants (13 females, 18 males; 27.3±7 years, 24.8±3.1 kg.m(2)) completed the study. There were no significant differences in any within or between day Parvo values or Cosmed values. When systems were compared, there was a significant difference between VE (Parvo2: 25.03 L/min, Cosmed2: 8.98 L/min) and FEO(2) (Parvo2: 19.68%, Cosmed2: 16.63%), however, there were no significant difference in device-calculated RMR (kcals/day).The Parvo and Cosmed are reliable metabolic system with no intra- or inter-day differences in RMR. Due to differences in measurement technology, FEO(2), V(E) were significantly different between systems, but the resultant RMR values were not significantly different.

BMI-referenced cut points for pedometer-determined steps per day in adults

BACKGROUND

The goal of this study was to establish preliminary criterion-referenced cut points for adult pedometer-determined physical activity (PA) related to weight status defined by body mass index (BMI).

METHODS

Researchers contributed directly measured BMI and pedometer data that had been collected (1) using a Yamax-manufactured pedometer, (2) for a minimum of 3 days, (3) on ostensibly healthy adults. The contrasting groups method was used to identify age- and gender-specific cut points for steps/d related to BMI cut points for normal weight and overweight/obesity (defined as BMI <25 and >or=25 kg/m2, respectively).

RESULTS

Data included 3127 individuals age 18 to 94 years (976 men, age = 46.8 +/- 15.4 years, BMI = 27.3 +/- 4.9; 2151 women, age = 47.4 +/- 14.9 years, BMI = 27.6 +/- 6.4; all gender differences NS). Best estimated cut points for normal versus overweight/obesity ranged from 11,000 to 12,000 steps/d for men and 8000 to 12,000 steps/d for women (consistently higher for younger age groups).

CONCLUSIONS

These steps/d cut points can be used to identify individuals at risk, or the proportion of adults achieving or falling short of set cut points can be reported and compared between populations. Cut points can also be used to set intervention goals, and they can be referred to when evaluating program impact, as well as environmental and policy changes.

Effects of body mass index on the accuracy of an electronic pedometer

Electronic pedometers are accurate for assessing steps taken while walking in normal weight adults but the accuracy of these devices has not been tested in overweight and obese men and women. The primary purpose of this study was to assess the accuracy of an electronic pedometer for measuring steps taken at various walking speeds in groups of adults with variations in body mass index (BMI). The secondary purpose was to determine if the manufacturer recommended position is the best placement position for overweight and obese adults. Participants were categorized into one of three BMI categories identified by the World Health Organization: normal (N = 25; < 25 kg x m(-2)), overweight (N = 24; 25 - 29.9 kg x m(-2)), or obese (N = 17; > or = 30 kg x m(-2)). Participants walked on a treadmill for 3 min at 54, 67, 80, 94, and 107 m x min(-1) for a total of 15 min. During the treadmill walking, three electronic pedometers tallied steps taken. The pedometers were placed at the waist level, one on the anterior mid-line of the thigh (front; manufacturer recommended placement), one on the mid-axillary line (side), and one on the posterior mid-line of the thigh (back). Concurrently, a researcher counted steps using a hand-tally counter. Category of BMI did not affect the accuracy of the pedometer at any walking speed (54 m x min(-1), p = 0.991; 67 m x min(-1), p = 0.556; 80 m x min(-1), p = 0.591; 94 m x min(-1), p = 0.426; 107 m x min(-1), p = 0.869). At 54 m x min(-1), the front, side, and back pedometers significantly underestimated hand-tally counted steps by 20 % (p < 0.001), 33 % (p < 0.001), and 26 % (p < 0.001), respectively. At 67 m x min(-1) the front, side, and back pedometers significantly underestimated hand-tally counted steps by 7 % (p = 0.027), 13 % (p < 0.001), 11 % (p = 0.002), respectively. The steps recorded by the electronic pedometers placed at the front, side and back of the waist were not significantly different than steps counted by the hand-tally counter at speeds of 80 m x min(-1) and higher for all subjects combined. An electronic pedometer accurately quantified steps walked at speeds of 80 m x min(-1) or faster in persons with a normal BMI and those classified as overweight or obese. The placement of the pedometer on the front, side or back of the waistband did not affect accuracy of the pedometer for counting steps.

Comparison of MTI accelerometer cut-points for predicting time spent in physical activity

The purpose of this study was to establish the accuracy of five published accelerometer regression equations that predict time spent in different intensity classifications during free-living activities. Ten participants completed physical tasks in a field setting for a near-continuous 5 - 6 h-period while oxygen uptake and accelerometer data were collected. The amount of time spent in resting/light, moderate and hard activity was computed from 3 and 6 MET cut-points associated with five existing regression formulas relating accelerometer counts x min -1 to energy expenditure. The Freedson cut-points over-estimated resting/light activity by 34 min (13 %) and under-estimated moderate activity by 38 min (60 %). The Hendelman cut-points for all activities underestimated resting/light activity by 77 min (29 %), and overestimated moderate activity by 77 min (120 %). The Hendelman cut-points developed from walking activities over-estimated resting/light activity by 37 min (14 %) and under-estimated moderate activity by 38 min (60 %). Estimates from the Swartz cut-points for estimating time spent in resting/light, moderate and hard intensity activity were not different from the criterion measure. The Nichols cut-points over-estimated resting/light activity by 31 min (12 %) and under-estimated moderate activity by 35 min (55 %). Even though the Swartz method did not differ from measured time spent in moderate activity on a group basis, on an individual basis, large errors were seen. This was true for all regression formulas. These errors highlight some of the limitations to using hip-mounted accelerometers to reflect physical activity patterns. The finding that different accelerometer cut-points gave substantially different estimates of time spent data has important implications for researchers using accelerometers to predict time spent in different intensity categories.

Relationship of leisure-time physical activity and occupational activity to the prevalence of obesity

OBJECTIVE

To assess the interaction between leisure-time physical activity (LTPA) and occupational activity (OA) on the prevalence of obesity.

DESIGN

Secondary data analysis of a population based cross-sectional US national sample (NHANES III).

SUBJECTS

A total of 4889 disease-free, currently employed adults over age 20 y.

MEASUREMENTS

Subjects body mass index (BMI) was categorized as (1) obese (BMI> or =30 kg/m(2)), or (2) non-obese (BMI<30 kg/m(2)). LTPA was divided into four categories: (1) no LTPA; (2) irregular LTPA; (3) regular moderate intensity LTPA; and (4) regular vigorous intensity LTPA. OA was grouped as (1) high OA and (2) low OA. Age, gender, race-ethnicity, smoking status, urbanization classification, alcohol consumption and income were statistically controlled.

RESULTS

In all, 16.8% (s.e. 0.7) of the total subject population were obese (15.1% (s.e. 1.1) of men and 19.1% (s.e. 1.1) of women). Logistic regression revealed that compared to those who engage in no LTPA and have low levels of OA, the likelihood of being obese is 42% (95% CI 0.35, 0.96) lower for those who engage in no LTPA and have high OA, 48% (95% CI 0.32, 0.83) lower for those who have irregular LTPA and have high levels of OA, and about 50% lower for all those who have regular LTPA through moderate or vigorous activity levels regardless of OA level.

CONCLUSION

When considering disease free adults above 20 y of age employed in high and low activity occupations, a high level of occupational activity is associated with a decreased likelihood of being obese.

Simultaneous heart rate-motion sensor technique to estimate energy expenditure

PURPOSE

Heart rate (HR) and motion sensors represent promising tools for physical activity (PA) assessment, as each provides an estimate of energy expenditure (EE). Although each has inherent limitations, the simultaneous use of HR and motion sensors may increase the accuracy of EE estimates. The primary purpose of this study was to establish the accuracy of predicting EE from the simultaneous HR-motion sensor technique. In addition, the accuracy of EE estimated by the simultaneous HR-motion sensor technique was compared to that of HR and motion sensors used independently.

METHODS

Thirty participants (16 men: age, 33.1 +/- 12.2 yr; BMI, 26.1 +/- 0.7 kg.m(-2); and 14 women: age, 31.9 +/- 13.1 yr; BMI, 27.2 +/- 1.1 kg.m(-2) (mean +/- SD)) performed arm and leg work in the laboratory for the purpose of developing individualized HR-VO2 regression equations. Participants then performed physical tasks in a field setting for 15 min each. CSA accelerometers placed on the arm and leg were to discriminate between upper and lower body movement, and HR was then used to predict EE (METs) from the corresponding arm or leg laboratory regression equation. A hip-mounted CSA accelerometer and Yamax pedometer were also used to predict EE. Predicted values (METs) were compared to measured values (METs), obtained via a portable metabolic measurement system (Cosmed K4b(2)).

RESULTS

The Yamax pedometer and the CSA accelerometer on the hip significantly underestimated the energy cost of selected physical activities, whereas HR alone significantly overestimated the energy cost of selected physical activities. The simultaneous HR-motion sensor technique showed the strongest relationship with VO(2) (R(2) = 0.81) and did not significantly over- or underpredict the energy cost (P = 0.341).

CONCLUSION

The simultaneous HR-motion sensor technique is a good predictor of EE during selected lifestyle activities, and allows researchers to more accurately quantify free-living PA.

Validity of inspiratory and expiratory methods of measuring gas exchange with a computerized system

The accuracy of a computerized metabolic system, using inspiratory and expiratory methods of measuring ventilation, was assessed in eight male subjects. Gas exchange was measured at rest and during five stages on a cycle ergometer. Pneumotachometers were placed on the inspired and expired side to measure inspired (VI) and expired ventilation (VE). The devices were connected to two systems sampling expired O(2) and CO(2) from a single mixing chamber. Simultaneously, the criterion (Douglas bag, or DB) method assessed VE and fractions of O(2) and CO(2) in expired gas (FE(O(2)) and FE(CO(2))) for subsequent calculation of O(2) uptake (VO(2)), CO(2) production (VCO(2)), and respiratory exchange ratio. Both systems accurately measured metabolic variables over a wide range of intensities. Though differences were found between the DB and computerized systems for FE(O(2)) (both inspired and expired systems), FE(CO(2)) (expired system only), and VO(2) (inspired system only), the differences were extremely small (FE(O(2)) = 0.0004, FE(CO(2)) = -0.0003, VO(2) = -0.018 l/min). Thus a computerized system, using inspiratory or expiratory configurations, permits extremely precise measurements to be made in a less time-consuming manner than the DB technique.

Evaluation of heart rate as a method for assessing moderate intensity physical activity

To further develop our understanding of the relationship between habitual physical activity and health, research studies require a method of assessment that is objective, accurate, and noninvasive. Heart rate (HR) monitoring represents a promising tool for measurement because it is a physiological parameter that correlates well with energy expenditure (EE). However, one of the limitations of HR monitoring is that training state and individual HR characteristics can affect the HR-VO2 relationship.

PURPOSE

The primary purpose of this study was to examine the relationship between HR (beats x min(-1)) and VO2 (mL x kg(-1 x -1) min(-1)) during field- and laboratory-based moderate-intensity activities. In addition, we examined the validity of estimating EE from HR after adjusting for age and fitness. This was done by expressing the data as a percent of heart rate reserve (%HRR) and percent of VO2 reserve (%VO2R).

METHODS

Sixty-one adults (18-74 yr) performed physical tasks in both a laboratory and field setting. HR and VO2 were measured continuously during the 15-min tasks. Mean values over min 5-15 were used to perform linear regression analysis on HR versus VO2. HR data were then used to predict EE (METs), using age-predicted HRmax and estimated VO2max.

RESULTS

The correlation between HR and VO2 was r = 0.68, with HR accounting for 47% of the variability in VO2. After adjusting for age and fitness level, HR was an accurate predictor of EE (r = 0.87, SEE = 0.76 METs).

CONCLUSION

This method of analyzing HR data could allow researchers to more accurately quantify physical activity in free-living individuals.

Compendium of physical activities: an update of activity codes and MET intensities

We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.

Validity of four motion sensors in measuring moderate intensity physical activity

PURPOSE

This study tested the validity of four motion sensors for measuring energy expenditure (EE) during moderate intensity physical activities in field and laboratory settings. We also evaluated the accuracy of the EE values for selected moderate activities listed in the 1993 Compendium of Physical Activities.

METHODS

A total of 81 participants (age 19-74 yr) completed selected tasks from six general categories: yardwork, housework, occupation, family care, conditioning, and recreation. Twelve individuals performed each of the 28 activities examined. During each activity, EE was measured using a portable metabolic measurement system. Participants also wore three accelerometers (Computer Science and Applications [CSA], Inc. model 7164; Caltrac; and Kenz Select 2) and the Yamax SW-701 electronic pedometer. For the CSA device, three previously developed regression equations were used to convert accelerometer scores to EE.

RESULTS

The mean error scores (indirect calorimetry minus device) across all activities were: CSA1, 0.97 MET; CSA2, 0.47 MET, CSA3, 0.05 MET; Caltrac, 0.83 MET; Kenz, 0.96 MET; and Yamax, 1.12 MET. The correlation coefficients between indirect calorimetry and motion sensors ranged from r = 0.33 to r = 0.62. The energy cost for power mowing and sweeping/mopping was higher than that listed in the 1993 Compendium (P < 0.05), and the cost for several household and recreational activities was lower (P < 0.05).

CONCLUSION

Motion sensors tended to overpredict EE during walking. However, they underpredicted the energy cost of many other activities because of an inability to detect arm movements and external work. These findings illustrate some of the limitations of using motion sensors to predict EE in field settings.

Estimation of energy expenditure using CSA accelerometers at hip and wrist sites

PURPOSE

This study was designed to establish prediction models that relate hip and wrist accelerometer data to energy expenditure (EE) in field and laboratory settings. We also sought to determine whether the addition of a wrist accelerometer would significantly improve the prediction of EE (METs), compared with a model that used a hip accelerometer alone.

METHODS

Seventy participants completed one to six activities within the categories of yardwork, housework, family care, occupation, recreation, and conditioning, for a total of 5 to 12 participants tested per activity. EE was measured using the Cosmed K4b2 portable metabolic system. Simultaneously, two Computer Science and Applications, Inc. (CSA) accelerometers (model 7164), one worn on the wrist and one worn on the hip, recorded body movement. Correlations between EE measured by the Cosmed and the counts recorded by the CSA accelerometers were calculated, and regression equations were developed to predict EE from the CSA data.

RESULTS

The wrist, hip, and combined hip and wrist regression equations accounted for 3.3%, 31.7%, and 34.3% of the variation in EE, respectively. The addition of the wrist accelerometer data to the hip accelerometer data to form a bivariate regression equation, although statistically significant (P = 0.002), resulted in only a minor improvement in prediction of EE. Cut points for 3 METs (574 hip counts), 6 METs (4945 hip counts), and 9 METs (9317 hip counts) were also established.

CONCLUSION

The small amount of additional accuracy gained from the wrist accelerometer is offset by the extra time required to analyze the data and the cost of the accelerometer.

Comparison of three methods for measuring the time spent in physical activity

PURPOSE

Three methods for measuring time spent in daily physical activity (PA) were compared during a 21-d period among 83 adults (38 men and 45 women).

METHODS

Each day, participants wore a Computer Science and Applications, Inc. (CSA) monitor and completed a 1-page, 48-item PA log that reflected time spent in household, occupational, transportation, sport, conditioning, and leisure activities. Once a week, participants also completed a telephone survey to identify the number of minutes spent each week in nonoccupational walking and in moderate intensity and hard/very hard-intensity PA. Data were analyzed using descriptive statistics and Spearman rank-order correlations. Three equations developed to compute CSA cut points for moderate and hard/very hard PA were also compared with the PA logs and PA survey.

RESULTS

There was modest to good agreement for the time spent in different PA intensity categories between the three CSA cut point methods (r = 0.43-0.94, P < 0.001). Correlations between the CSA and PA logs ranged from r = 0.22 to r = 0.36, depending on the comparisons. Correlations between the survey items and PA logs were r = 0.26-0.54 (P < 0.01) for moderate and walking activities and r < 0.09 (P > 0.05) for hard/very hard activities. Correlations between the survey items and the CSA min per day varied according to the method used to compute the CSA intensity cut points.

CONCLUSIONS

The results were consistent with findings from other PA validation studies that show motion sensors, PA logs, and surveys reflect PA; however, these methods do not always provide similar estimates of the time spent in resting/light, moderate, or hard/very hard PA.